2019-10-28

Credits

BaseR plotting

  • Don’t look as good

  • Hard to build more complex plots, and fine-tune

Grammar of graphics

  • Wilkinson (2005) laid out “Grammar of Graphics”

ggplot2

  • Hadley Wickham implemented grammar in R package ggplot2

ggplot2 philosophy

What is a statistical graphic?

  • Take variables from a dataset

  • map them to aes()thetic attributes

  • geom_etric objects

Example with Gapminder data

How are variables mapped to aesthetic attributes of points?

How to use it?

Construct a graphic by adding modular pieces

  • ggplot(data, mapping)

  • Add ‘layers’ of geometric objects

  • Adjustments to axis scales, colors, labels, asthetic mods

  • “Chaining” together components (use + rather than %>%)

Let’s try a scatterplot

gap_92 <- gapminder %>% 
  filter(year == 1992) %>% 
  mutate(gdp = gdpPercap * pop / 1e9) 
gap_92 %>% head(4)
## # A tibble: 4 x 7
##   country     continent  year lifeExp      pop gdpPercap    gdp
##   <chr>       <chr>     <int>   <dbl>    <int>     <dbl>  <dbl>
## 1 Afghanistan Asia       1992    41.7 16317921      649.  10.6 
## 2 Albania     Europe     1992    71.6  3326498     2497.   8.31
## 3 Algeria     Africa     1992    67.7 26298373     5023. 132.  
## 4 Angola      Africa     1992    40.6  8735988     2628.  23.0

Let’s try a scatterplot

ggplot(gap_92, mapping = aes(x = gdp, y = lifeExp)) + 
  geom_point()

Adding more aesthetic mappings

ggplot(gap_92, mapping = aes(x = gdp, y = lifeExp, shape = continent)) + 
  geom_point()

Scales

ggplot(gap_92, mapping = aes(x = gdp, y = lifeExp)) + 
  geom_point() +
  scale_x_log10()

  • Change scaling of x- and y-axes (also reverse axes)

  • Change which colors etc. get applied to which values

Labels

  • labs function adds custom axis labels and titles
ggplot(gap_92, mapping = aes(x = gdp, y = lifeExp)) + 
  geom_point() +
  scale_x_log10() +
  labs(x = 'Gross Domestic Product (Billions $)',
       y = 'Life Expectancy at birth (years)',
       title = 'Gapminder for 1992')

Practice

  • Try to make this plot

Key geoms

  • Scatterplot: geom_point()

  • Line graph: geom_line()

  • Smoothing functions: geom_smooth()

  • Text: geom_text()

  • Histogram: geom_histogram()

  • Boxplot: geom_boxplot

  • Bar graph: geom_bar()

geom_line

df <- gapminder %>% 
  filter(country == 'Romania') 
ggplot(df, mapping = aes(x = year, y = lifeExp)) + 
  geom_line()

Layering geoms

We can add as many geoms to a plot as we want, stacked on as ‘layers’ in order

ggplot(df, mapping = aes(x = year, y = lifeExp)) + 
  geom_line() +
  geom_point()

What if we had multiple data points per year?

df <- gapminder %>% 
  filter(country %in% c('Romania', 'Thailand'))
ggplot(df, mapping = aes(x = year, y = lifeExp)) + 
  geom_line() +
  geom_point()

Need to separate them by country (group aesthetic)

ggplot(df, mapping = aes(x = year, y = lifeExp, group = country)) + 
  geom_line() +
  geom_point()

Often useful to color lines by group, use color aesthetic with a categorical variable and it automatically groups

ggplot(df, mapping = aes(x = year, y = lifeExp, color = country)) + 
  geom_line() +
  geom_point()

Multiple aesthetic mappings

  • Set default mapping in ggplot but can override this for individual geoms
ggplot(df, mapping = aes(x = year, y = lifeExp)) + 
  geom_line(mapping = aes(color = country)) +
  geom_point()

Multiple aesthetic mappings

  • You can ‘hard-code’ aesthetic properties for each geom
ggplot(df, mapping = aes(x = year, y = lifeExp, color = country)) + 
  geom_line(linetype = 2, size = 1.5) +
  geom_point(color = 'black', size = 3, alpha = 0.75)

Some notes on using color

If we map a continuous variable to color it won’t group automatically

ggplot(df, mapping = aes(x = year, y = lifeExp, color = gdpPercap)) + 
  geom_line() +
  geom_point(size = 3)

Some notes on using color

We need to specify group manually

ggplot(df, mapping = aes(x = year, y = lifeExp, 
                         group = country, color = gdpPercap)) + 
 geom_line() +
  geom_point(size = 3)

Some notes on using color

  • Assume continuous map for numeric data, discrete map for strings

  • Make numeric data into factors if you want discrete colors

my_df <- gapminder %>% 
  filter(year %in% c(1957, 1977, 1997)) 
ggplot(my_df, mapping = aes(x = gdpPercap, y = lifeExp, color = factor(year))) + 
  geom_point() +
  scale_x_log10() +
  labs(color = 'year')

Color palettes

We can use scale_color_manual to set the color of each group manually

my_cols <- c(Romania = 'green', Thailand = 'orange')

ggplot(df, mapping = aes(x = year, y = lifeExp, color = country)) + 
  geom_line() +
  scale_color_manual(values = my_cols)

scale_color_brewer offers some useful default color schemes

ggplot(df, mapping = aes(x = year, y = lifeExp, color = country)) + 
  geom_line() +
  scale_color_brewer(palette = 'Dark2')

Rcolorbrewer

when geom_line doesn’t work

ggplot(gap_92, mapping = aes(x = gdp, y = lifeExp)) + 
  geom_line() +
  geom_point() + 
  scale_x_log10() +
  labs(x = 'Gross Domestic Product (Billions $)',
       y = 'Life Expectancy at birth (years)')

geom_smooth

ggplot(gap_92, mapping = aes(x = gdp, y = lifeExp)) + 
  geom_point() + 
  geom_smooth() +
  scale_x_log10() +
  labs(x = 'Gross Domestic Product (Billions $)', y = 'Life Expectancy at birth (years)')

geom_smooth (linear regression)

ggplot(gap_92, mapping = aes(x = gdp, y = lifeExp)) + 
  geom_point() + 
  geom_smooth(method = 'lm') +
  scale_x_log10() +
  labs(x = 'Gross Domestic Product (Billions $)', y = 'Life Expectancy at birth (years)')

geom_smooth to simplify plots

ggplot(gapminder, mapping = aes(x = year, y = lifeExp, color = continent)) + 
  geom_point()

geom_smooth to simplify plots

ggplot(gapminder, mapping = aes(x = year, y = lifeExp, color = continent)) + 
  geom_smooth()

Practice

Facets

gap_early <- gapminder %>% 
  filter(year < 1970)
ggplot(gap_early, mapping = aes(x = gdpPercap, y = lifeExp)) + 
  geom_point() + 
  geom_smooth(se = FALSE) +
  scale_x_log10() +
  facet_wrap(~continent)

ggplot(gap_early, mapping = aes(x = gdpPercap, y = lifeExp)) + 
  geom_point() + 
  geom_smooth(se = FALSE) +
  scale_x_log10() +
  facet_grid(year ~ continent)

Practice

geom_text

ggplot(gap_df, mapping = aes(x = gdp, y = lifeExp, label = country)) + 
  geom_point() + 
  geom_smooth(method = 'lm', se = FALSE) +
  scale_x_log10() +
  geom_text() +
  labs(x = 'Gross Domestic Product (Billions $)', y = 'Life Expectancy at birth (years)')

ggrepel

library(ggrepel)
ggplot(gap_df, mapping = aes(x = gdp, y = lifeExp)) + 
  geom_point() + 
  geom_smooth(method = 'lm', se = FALSE) +
  scale_x_log10() +
  labs(x = 'Gross Domestic Product (Billions $)', y = 'Life Expectancy at birth (years)') + 
  geom_label_repel(aes(label = country), size = 2.5)

geom_histogram

ggplot(gapminder, mapping = aes(x = gdpPercap)) +
  geom_histogram()

ggplot(gapminder, mapping = aes(x = gdpPercap)) +
  geom_histogram() +
  scale_x_log10()

geom_histogram

  • Change number of bins (can specify details of bin positions)
gapminder %>% 
  ggplot(aes(gdpPercap)) +
  geom_histogram(bins = 100) +
  scale_x_log10()

geom_histogram

  • Encode different continents in different colors
ggplot(gapminder, mapping = aes(x = gdpPercap, color = continent)) +
  geom_histogram() +
  scale_x_log10()

fill vs color

ggplot(gapminder, mapping = aes(x = gdpPercap, fill = continent)) +
  geom_histogram() +
  scale_x_log10()

geom_density

ggplot(gapminder, mapping = aes(x = gdpPercap)) +
  geom_density() +
  scale_x_log10()

geom_density

  • Separate by continent and give spearate fill colors
ggplot(gapminder, mapping = aes(x = gdpPercap, fill = continent)) +
  geom_density(alpha = 0.5) +
  scale_x_log10()

geom_boxplot

ggplot(gapminder, mapping = aes(x = continent, y = gdpPercap)) +
  geom_boxplot() +
  scale_y_log10()

geom_violin

ggplot(gapminder, mapping = aes(x = continent, y = gdpPercap)) +
  geom_violin() +
  scale_y_log10()

What if I want to control the order?

  • Need to use the idea of a factor

  • Factors used to encode categorical variables, specify the possible ‘levels’, and optionally an ordering

cont_order <- c('Oceania', 'Europe', 'Americas', 'Asia', 'Africa')
gap_cat <- gapminder %>% 
  mutate(continent = factor(continent, levels = cont_order))

What if I want to control the order?

ggplot(gap_cat, mapping = aes(x = continent, y = gdpPercap)) +
  geom_violin() +
  scale_y_log10()

Geom_beeswarm

library(ggbeeswarm)
ggplot(gap_cat, mapping = aes(x = continent, y = gdpPercap)) +
  geom_beeswarm(size = 0.5, alpha = 0.75) +
  scale_y_log10()

Quick test

head(mtcars, 5)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2

Now I want to make a boxplot of horsepower for cars with different numbers of cylinders

ggplot(mtcars, mapping = aes(x = cyl, y = hp)) + 
  geom_boxplot()

ggplot(mtcars, mapping = aes(x = factor(cyl), y = hp)) + 
  geom_boxplot()

geom_bar

  • This gives an error
gap_82 <- gapminder %>% 
  filter(year == 1982, continent == 'Americas')
ggplot(gap_82, mapping = aes(x = country, y = gdpPercap)) + 
  geom_bar()

geom_bar

  • Need stat = 'identity'
ggplot(gap_82, mapping = aes(x = country, y = gdpPercap)) + 
  geom_bar(stat = 'identity')

theme

  • You can customize MANY details of the plot using the theme function

  • It’s a bit complicated at first, but most common changes are easy to google.

Saving your plots

  • ggsave
ggplot(gapminder, mapping = aes(x = continent, y = gdpPercap)) +
  geom_violin() +
  scale_y_log10()
ggsave(filename = here::here('results', 'my_fig.png'))
  • Using the Rstudio GUI

Practice

plotly

  • Tool for making interactive plots in R

  • Can convert ggplot2 plots into interactive form using the ggplotly function

Practice

Resources

Beautification

ggpubr

library(ggpubr)
my_comparisons <- list( c("Africa", "Asia"), c('Europe', 'Oceania'))
ggplot(gapminder, mapping = aes(x = continent, y = gdpPercap)) +
  geom_violin() +
  scale_y_log10() +
  stat_compare_means(method = 'wilcox.test', comparisons = my_comparisons)

cowplot

Great tool for combining multiple ‘panels’ into one plot

library(cowplot)

p1 <- ggplot(mtcars, aes(disp, mpg)) + 
  geom_point()
p2 <- ggplot(mtcars, aes(qsec, mpg)) +
  geom_point()
plot_grid(p1, p2, labels = c('A', 'B'))

complexheatmap